New artificial intelligence-based technology can diagnose pneumonia by listening to someone cough, and scientists say if rolled out, people could diagnose themselves without going to the doctor, and health costs should decline.
The method uses artificial intelligence to work out whether the sound of a cough suggests someone is suffering from the deadly respiratory condition, which particularly affects older people and very young children, reports The Independent.
It is crucial that the disease is caught as early as possible to give sufferers the best chance of recovering.
Researcher Jin Yong Jeon from Hanyang University in South Korea said: “Automatically diagnosing a health condition through information on coughing sounds that occur continuously during daily life will facilitate non-face-to-face treatment. It will also be possible to reduce overall medical costs.
“Our research team is planning to automate each step-by-step process that is currently performed manually to improve convenience and applicability.”
Existing ways of diagnosing pneumonia consist of a range of blood tests and chest scans, and a doctor needs to suspect someone has the illness before treatment can begin.
Regarding the technology used in the study, the scientists said that every room and recording device is different, which means the devices are fitted with room impulse responses. These measure how the acoustics of a space react to different sound frequencies.
By combining this information with the recorded cough sounds, the algorithm can work in any environment. Plans are already in place to apply the algorithm so patients can be monitored at home, they said, and the team is also looking to develop an app based on it for social care at home.
"Our research team is also planning to automate each step-by-step process that is currently performed manually to improve convenience and applicability.”
The findings were presented at the 183rd meeting of the Acoustical Society of America in Nashville, Tennessee last week.
Study details
Diagnosis of Pneumonia by Cough Sounds Analyzed with Statistical Features and AI
Youngbeen Chung, Jie Jin, Hyun In Jo, Hyun Lee, Sang-Heon Kim, Sung Jun Chung, Ho Joo Yoon, Junhong Park, and Jin Yong Jeon.
Abstract
Pneumonia is a serious disease often accompanied by complications, sometimes leading to death. Unfortunately, diagnosis of pneumonia is frequently delayed until physical and radiologic examinations are performed. Diagnosing pneumonia with cough sounds would be advantageous as a non-invasive test that could be performed outside a hospital. We aimed to develop an artificial intelligence (AI)-based pneumonia diagnostic algorithm. We collected cough sounds from thirty adult patients with pneumonia or the other causative diseases of cough. To quantify the cough sounds, loudness and energy ratio were used to represent the level and its spectral variations. These two features were used for constructing the diagnostic algorithm. To estimate the performance of developed algorithm, we assessed the diagnostic accuracy by comparing with the diagnosis by pulmonologists based on cough sound alone. The algorithm showed 90.0% sensitivity, 78.6% specificity and 84.9% overall accuracy for the 70 cases of cough sound in pneumonia group and 56 cases in non-pneumonia group. For same cases, pulmonologists correctly diagnosed the cough sounds with 56.4% accuracy. These findings showed that the proposed AI algorithm has value as an effective assistant technology to diagnose adult pneumonia patients with
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